Literature DB >> 27572425

Chemical space visualization: transforming multidimensional chemical spaces into similarity-based molecular networks.

Antonio de la Vega de León1, Jürgen Bajorath1.   

Abstract

BACKGROUND: The concept of chemical space is of fundamental relevance for medicinal chemistry and chemical informatics. Multidimensional chemical space representations are coordinate-based. Chemical space networks (CSNs) have been introduced as a coordinate-free representation.
RESULTS: A computational approach is presented for the transformation of multidimensional chemical space into CSNs. The design of transformation CSNs (TRANS-CSNs) is based upon a similarity function that directly reflects distance relationships in original multidimensional space.
CONCLUSION: TRANS-CSNs provide an immediate visualization of coordinate-based chemical space and do not require the use of dimensionality reduction techniques. At low network density, TRANS-CSNs are readily interpretable and make it possible to evaluate structure-activity relationship information originating from multidimensional chemical space.

Keywords:  SAR information; chemical space; chemical space networks; chemical space transformation; coordinate-based representation; distance relationships; molecular similarity; visualization

Mesh:

Year:  2016        PMID: 27572425     DOI: 10.4155/fmc-2016-0023

Source DB:  PubMed          Journal:  Future Med Chem        ISSN: 1756-8919            Impact factor:   3.808


  5 in total

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3.  Chemical space exploration guided by deep neural networks.

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Journal:  RSC Adv       Date:  2019-02-11       Impact factor: 4.036

4.  Progress on open chemoinformatic tools for expanding and exploring the chemical space.

Authors:  José L Medina-Franco; Norberto Sánchez-Cruz; Edgar López-López; Bárbara I Díaz-Eufracio
Journal:  J Comput Aided Mol Des       Date:  2021-06-18       Impact factor: 4.179

5.  Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.

Authors:  Longendri Aguilera-Mendoza; Yovani Marrero-Ponce; César R García-Jacas; Edgar Chavez; Jesus A Beltran; Hugo A Guillen-Ramirez; Carlos A Brizuela
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  5 in total

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